Cover Image

FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Peng, Hongyi, Yu, Han, Tang, Xiaoli, Li, Xiaoxiao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2023)

pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
by: Yi, Liping, et al.
Published: (2023)

FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)

pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2024)

FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
by: Ma, Jeffrey, et al.
Published: (2024)

FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
by: Li, Yunbo, et al.
Published: (2025)

FedMT: Federated Learning with Mixed-type Labels
by: Zhang, Qiong, et al.
Published: (2022)

FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
by: Li, Shiwei, et al.
Published: (2024)

FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
by: Zhan, Ziwei, et al.
Published: (2024)

DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by: Zhu, Guogang, et al.
Published: (2024)

FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
by: Guo, Fucheng, et al.
Published: (2025)

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA
by: Jhunjhunwala, Divyansh, et al.
Published: (2025)

FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)

Federated Model Heterogeneous Matryoshka Representation Learning
by: Yi, Liping, et al.
Published: (2024)

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by: Liang, Han, et al.
Published: (2024)

FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)

FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
by: Han, Pengchao, et al.
Published: (2023)

Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness
by: Li, Boyuan, et al.
Published: (2024)

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
by: Chen, Yuanyuan, et al.
Published: (2022)

LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning
by: Zhang, Li, et al.
Published: (2025)

Orthogonal Calibration for Asynchronous Federated Learning
by: Zhang, Jiayun, et al.
Published: (2025)

FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters
by: Ishii, Hiro, et al.
Published: (2025)

FedHB: Hierarchical Bayesian Federated Learning
by: Kim, Minyoung, et al.
Published: (2023)

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
by: Li, Shiwei, et al.
Published: (2025)

FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
by: Jiang, Yu, et al.
Published: (2024)

FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
by: Zhou, Liuzhi, et al.
Published: (2024)

FedOptima: Optimizing Resource Utilization in Federated Learning
by: Zhang, Zihan, et al.
Published: (2025)

FedRIR: Rethinking Information Representation in Federated Learning
by: Huang, Yongqiang, et al.
Published: (2025)

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
by: Zuo, Shiyuan, et al.
Published: (2024)

Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
by: Xu, Jiahao, et al.
Published: (2024)

FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
by: Duan, Moming, et al.
Published: (2020)

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
by: Tastan, Nurbek, et al.
Published: (2024)

Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
by: Li, Zhe, et al.
Published: (2024)

Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
by: Li, Yi, et al.
Published: (2026)

FedBaF: Federated Learning Aggregation Biased by a Foundation Model
by: Park, Jong-Ik, et al.
Published: (2024)

FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
by: Yan, Qifan, et al.
Published: (2025)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
by: Chen, Yiyue, et al.
Published: (2025)

FedZero: Leveraging Renewable Excess Energy in Federated Learning
by: Wiesner, Philipp, et al.
Published: (2023)

FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
by: Li, Li, et al.
Published: (2021)

Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
by: Ma, Mengmeng, et al.
Published: (2024)